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Development and Implementation of Multiple Model Filters for Online Identification and Compensation of Atmospheric Disturbances in Automatic Landing of Fixed Wing UAV

Sharifi, Alireza | 2020

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 53131 (45)
  4. University: Sharif University of Technology
  5. Department: Aerospace Engineering
  6. Advisor(s): Nobahari, Hadi
  7. Abstract:
  8. In this study, a multiple model wind estimator is proposed to detect the wind type and to estimate the wind components as well as the states of a fixed-wing UAV without any direct measurement of the air data. Then, the identified wind model and the estimated states are compensated in the heuristic nonlinear model predictive controller during landing phase. For this purpose, a static multiple model approach is taken, comprised of four independent extended Kalman filters, each one is estimating the wind based on one of the four wind models including a constant wind, a “1-cosine” model, a wind shear and a microburst.Moreover, a new heuristic multiple model filter, called Multiple Model Extended Continuous Ant Colony Filter, is proposed to find the best wind model among a set of wind models and to estimate the states of the UAV. In this filter, a colony of virtual ants search the state space stochastically and dynamically for each model. Pheromone distribution attracts the ants toward the true model and the true states.Then, observability of the states and the wind components are analyzed. Four new propositions are introduced and proved for unknown input observability, state and unknown input observability, the effect of time-varying unknown input matrix on the unknown input observability, and the effect of linearization errors on the state observability. Moreover, observability of the wind parameters is analyzed using the theory of nonlinear systems observability.Performance of the proposed multiple model filters is also evaluated in maneuvering flight and compared to a single Kalman filter and a single extended continuous ant colony filter. The results show that the proposed approach provides excellent performance in estimating the states, the wind model and its parameters.Then, the outputs of the proposed filtring algorithms are utilized and compensated in the heuristic nonlinear model predictive controller based on the particle swarm optimization. Moreover, stability of this controller is introduced and proved. Finally, a hardware-in-the-loop experiment is also performed to verify the real-time implementation capability of the suggested architectures. The results show that the proposed algorithm effectively improves the controller performance with the wind compensation and the UAV lands accurately.
  9. Keywords:
  10. Wind Model ; Hardware in the Loop ; Heuristic Filter ; Unmanned Aerial Vehicles (UAV) ; Nonlinear Observer ; Model Predictive Control ; Wind Estimation

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